AISSAI - Heterogeneous Data and Large Representation Models in Science

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A graph-structured distance for heterogeneous datasets with meta variables

Automatic estimation of the wind turbine noise with recurrent neural networks

Beyond Gauss? A more accurate model for LISA astrophysical noise sources

Challenges of heterogeneous data for building Linguistic Theory

Closing words & Farewell

Enhancing Ultrasound Localization Microscopy (ULM) with Spatio-Temporal Deep Learning

Explaining Jet Flavour Taggers with Integrated Gradients

Galaxy detection with deep learning in radio data

Graph Neural Networks for track reconstruction in the ATLAS ITk detector

Identifying a piecewise affine signal from its nonlinear observation - application to DNA replication analysis

Keynote Address: A causal perspective on reliable and interpretable representation learning

Keynote Address: Deep learning and the global workspace theory

Keynote Address: Foundation models for high energy physics

Keynote Address: Gravitational waves coming at you from all directions

Keynote Address: Medium Range Weather Forecasting with Machine Learning

Keynote Address: Multimodal Pretraining for Astrophysical Foundation Models

Keynote Address: RELEO - Representation Learning for Earth Observation

Large-scale deep-learning for weather and climate prediction

Learning how to design biomolecules using a neuro-symbolic architecture

Leveraging AI in computational physics with NVIDIA Modulus and TorchFort

Neural density estimation for Galactic Binaries in LISA data analysis

Optimizing PyTorch: Accelerating Training and Inference with Compilation, Custom Kernels, and Beyond

PolarBERT: a Foundation Model for Neutrino Telescope Data

Preprocessing arbitrarily structured data for AI with Awkward Array

Round Table

Salt: Multimodal, Multitask Models for the ATLAS Experiment

Scientific Foundation Models for Computational Fluid Dynamics: threats and opportunities

Searching for Dark Matter at the LHC with GNN

Semi-supervised multimodal representation learning through a global workspace

Small thinks big: transfer learning in KM3NeT/ORCA for neutrino event reconstruction

Statistically principled learning for gravitational-wave inverse problems

Welcome from the L2IT director

Welcome from the organizers; practicalities

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30/09/2024

01/10/2024

02/10/2024

03/10/2024

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